Johannes Schrott, Rainer Meindl, Christian Lettner, Wolfram Wöß, Lisa Ehrlinger,
"DQD: The Data Quality Definition Ontology"
, in Emmanouel Garoufallou, Fabio Sartori: 17th Research Conference Metadata and Semantic Research (MTSR) 2023, Serie Communications in Computer and Information Science, Vol. 2048, Springer Nature, Cham, Switzerland, Seite(n) 291-297, 7-2024
Original Titel:
DQD: The Data Quality Definition Ontology
Sprache des Titels:
Deutsch
Original Buchtitel:
17th Research Conference Metadata and Semantic Research (MTSR) 2023
Original Kurzfassung:
Data quality measurement is a prerequisite to assure and improve the quality of data, thus enabling reliable decision-making. In alignment with the ?fitness for use? definition, most existing solutions for measuring data quality are either (1) tailored to a specific use case and cannot be easily reused, or (2) too general, i.e., they do not provide specific metrics and require a high level of user involvement to be implemented. To address this research gap, we developed the Data Quality Definition (DQD) ontology, which is based on semantic web standards and provides a framework on how to define data quality metrics. DQD is intended to be used as input for data quality measurement tools and enables reusing domain knowledge defined in external sources (e.g., ontologies). Using DQD allows comparing data quality measurement results between different tools, use cases, data assets, and over time. In this paper, we introduce the structure of DQD and show its applicability with an example.
Sprache der Kurzfassung:
Englisch
Veröffentlicher:
Springer Nature
Verlagsanschrift:
Cham, Switzerland
Serie:
Communications in Computer and Information Science
Forschungs-